Exploration of Reported Complaints

Here is a quick exploration of the temporal trends in call volume when grouped by complaint type.

We start with a function just to make it easier to manually select different sets of complaints to view.

## Function to plot subset of complaints in a faceted plot
plot_complaint_volume <- function(data, vars) {
  
  plot_data <- data %>% filter(incident_complaint_reported_by_dispatch %in% vars) %>%
    mutate(mo_yr = dmy(paste0("01-", mo, "-", yr))) %>%
    group_by(mo_yr, incident_complaint_reported_by_dispatch) %>%
    count()
  
  cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#CC79A7", "#F0E442", "#0072B2", "#D55E00")
  
  p <- ggplot(plot_data) + 
    geom_line(aes(x = mo_yr, y = n, color = incident_complaint_reported_by_dispatch), alpha = 1) + 
    geom_point(aes(x = mo_yr, y = n, color = incident_complaint_reported_by_dispatch), alpha = 0.7) +
    geom_vline(xintercept = dmy("02-01-2020"), linetype = "dashed", alpha = 0.8) +
    scale_color_manual(values = cbPalette) +
    facet_wrap(~incident_complaint_reported_by_dispatch, ncol = 1) +
    labs(x = "Date", y = "Monthly Incidents", color = "Complaint")
  
  return(p)
}

For now, let's look at the top 3 complaints by frequency (breathing problems, sick person, and falls).

## View top 3 complaints
top_complaints <- ems_full %>% 
  group_by(incident_complaint_reported_by_dispatch) %>% 
  summarize(n = n()) %>% 
  top_n(3, n)

complaints <- top_complaints$incident_complaint_reported_by_dispatch

## Plot
plot_complaint_volume(ems_full, vars = complaints) + 
  labs(title = "Trends in Top 3 Complaints: Charlottesville") + 
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")

Plotting without faceting quickly gets too busy, but converting to an interactive layout with plotly can help with this by allowing for highlighting. Here are the top 8 complaints plotted together for better comparison.

#vars <- complaints
top_complaints <- ems_full %>% 
  group_by(incident_complaint_reported_by_dispatch) %>% 
  summarize(n = n()) %>% 
  top_n(8, n)

vars <- top_complaints$incident_complaint_reported_by_dispatch

plot_data <- ems_full %>% filter(incident_complaint_reported_by_dispatch %in% vars) %>%
  mutate(mo_yr = dmy(paste0("01-", mo, "-", yr))) %>%
  group_by(mo_yr, incident_complaint_reported_by_dispatch) %>%
  count()

# cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#CC79A7", "#F0E442", "#0072B2", "#D55E00")

## Key to tell plotly what variable to highlight by
key <- highlight_key(plot_data, ~incident_complaint_reported_by_dispatch)

## Create ggplot
plt <- ggplot(key, aes(text = paste('Complaint', incident_complaint_reported_by_dispatch, sep = ": "))) +
  geom_line(aes(x = mo_yr, y = n, color = incident_complaint_reported_by_dispatch), alpha = 1) + 
  #geom_point(aes(x = mo_yr, y = n), alpha = 0.7) +
  geom_vline(xintercept = dmy("02-01-2020"), linetype = "dashed", alpha = 0.8) +
  scale_color_brewer(palette = "Set2") +
  labs(title = "Call Volume by Reported Complaint", x = "Date", y = "Incidents per month") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

## Convert to plotly and set highlight options
plt_highlight <- ggplotly(plt, tooltip = c("text")) %>% 
  highlight(on = "plotly_hover", off = "plotly_doubleclick") %>%
  layout(showlegend = FALSE)

plt_highlight